Latest news with #TristanHandy
Techday NZ
5 days ago
- Business
- Techday NZ
dbt Labs unveils AI tools to boost data autonomy & governance
dbt Labs has released a suite of AI-powered features aimed at providing data analysts with greater autonomy without compromising data governance. The newly launched capabilities include dbt Canvas, a visual drag-and-drop model development tool; dbt Insights, an AI-assisted query environment; and an enhanced dbt Catalog for data asset discovery. These features are designed to support analysts with varying technical skills, offering both natural language and visual interfaces within a version-controlled setting managed by dbt workflows. The announcement comes as many organisations grapple with the balance between data self-service and the need for strong governance, a concern highlighted by a recent Gartner prediction stating, "by 2027, 60% of organisations will fail to realise the full value of their AI use cases due to fragmented data governance frameworks." Gartner identifies the proliferation of ungoverned data workflows among analysts as a driver for compliance risks, increased operational costs, and compromised data quality. Tristan Handy, Founder and Chief Executive Officer of dbt Labs, commented on the issue, saying, "Data teams today face a fundamental tension – analysts need speed and independence, while organisations require strong governance and security. Our new AI-powered solutions break down these traditional barriers for data analysts across any skill level and collaborate with developers in the same platform, which will have a significant, positive impact throughout the business." At the centre of the launch is dbt Canvas, which provides users who prefer drag-and-drop interfaces the ability to model and edit data. Integrated with dbt Copilot, it leverages natural language processing to help teams with limited SQL experience construct data models efficiently. The tool preserves organisational data governance and quality standards while promoting team collaboration and productivity. dbt Canvas is now generally available. dbt Insights, currently in preview, allows analysts to query, validate, and visualise data using either SQL or plain English. The tool takes into account a company's data models, lineage, and governance protocols, helping users conduct analysis and share insights within a governed workspace, reducing reliance on centralised data teams to fulfil requests. The expanded dbt Catalog now offers a consolidated search and exploration experience across Snowflake assets, even those not previously managed in dbt. This feature streamlines discovery and helps analysts understand and trust their data sources without moving between different platforms. While the catalogue is generally available, Snowflake asset exploration remains in preview, with further integrations promised. Dan Jewett, Senior Vice President, Product Management at Tableau, welcomed the update, stating, "Lowering the technical barrier to entry for data analysts has been important to Tableau from the beginning of the company. dbt's expanded offering is a game changer for customers that are looking to reduce the sizable burden on their data engineering teams, while simultaneously enabling analysts across the business in a meaningful way. It's a massive step forward for the future of data teams and one we're thrilled to continue to partner on." The company's customer WHOOP, a health monitoring technology provider, noted the impact of these tools on analyst self-service. William Tsu, Senior Analytics Engineer at WHOOP, said, "As our data needs evolve, empowering analysts with seamless self-exploration becomes increasingly critical. By keeping them within the familiar dbt Catalog they already use daily, dbt's new analyst offerings enhance discoverability and enable faster, more intuitive, and governed self-service." dbt Labs systems integrator InterWorks sees value in these changes for organisations seeking to scale analytics. James Wright, Chief Strategy Officer at InterWorks, commented, "dbt Canvas is unlocking a future where analysts can build confidently alongside engineers within the same trusted and governed workflows. We're excited about how this new development environment will help our customers unlock true self-service while maintaining the standards, security, and collaboration required to scale analytics responsibly." New cost management tools have also been introduced, with a dashboard that offers organisations visibility over data warehouse expenditure, project-level consumption, and cost-saving opportunities achieved through standardisation on dbt. This cost management dashboard, powered by the dbt Fusion engine, is in preview for Snowflake users. The dbt Fusion engine, recently developed following dbt Labs' acquisition of SDF Labs, underpins these updates. According to dbt Labs, Fusion delivers significantly improved performance, including data transformation speeds up to 30 times faster than previous iterations. The engine also introduces enhanced developer capabilities such as real-time code feedback and more efficient use of warehouse computing resources.
Yahoo
6 days ago
- Business
- Yahoo
dbt Labs Launches AI-Powered Features to Onboard Data Analysts into dbt
Analysts can now build, explore, and validate models leveraging the power of dbt PHILADELPHIA, May 28, 2025 /PRNewswire/ -- dbt Labs, the leader in standards for AI-ready structured data, today announced a powerful new suite of AI-enhanced features that give data analysts a fast and governed way to explore data and deliver insights within dbt's workflows. These new capabilities empower analysts across a range of technical backgrounds to lean on natural language or visual interfaces to build, explore and validate data in the same version-controlled environment trusted by data teams. This release includes dbt Canvas (a visual, drag-and-drop interface for model development), dbt Insights (an AI-powered query tool for quick analysis and sharing), and an enhanced dbt Catalog (for global asset discovery). Additionally, organizations can now use the new cost management dashboard to optimize their data warehouse spend. Bridging the gap between self-service and governance Gartner® predicts that, "by 2027, 60% of organizations will fail to realize the full value of their AI use cases due to fragmented data governance frameworks."* One contributing factor is the rise of ungoverned data workflows, often driven by analysts working around limited engineering support. To get the insights they need, data analysts rely on unsupported, disconnected tools and un-tested, bespoke logic to build, query, and explore data, leading to compliance risks, increased costs, and poor data quality that undermine organizational decision making. dbt's new AI-powered capabilities are purpose-built to solve this issue by giving analysts greater autonomy while ensuring every action remains governed, version-controlled and aligned with organizational data standards. "Data teams today face a fundamental tension – analysts need speed and independence, while organizations require strong governance and security," said Tristan Handy, founder and CEO of dbt Labs. "Our new AI-powered solutions break down these traditional barriers for data analysts across any skill level and collaborate with developers in the same platform, which will have a significant, positive impact throughout the business." Unlocking trusted self-service for analysts with dbt The Analytics Development Lifecycle (ADLC) is a vendor-agnostic framework that helps organizations mature how they build, maintain, and scale trusted data products. As the data control plane for the modern enterprise, dbt brings the ADLC to life, enabling version-controlled, governed workflows that power analytics across teams. dbt Labs is now making it easy for downstream analysts to participate in the ADLC with the following new capabilities: dbt Canvas, a new visual editing environment in dbt, enables users more comfortable with drag-and-drop tooling to build and edit data models. Analysts can describe what they want to build in natural language using dbt Copilot, allowing teams with limited SQL knowledge to build effective data models using context-rich AI. It automatically maintains governance and quality standards, while reducing reliance on data engineers, boosting collaboration and improving productivity. dbt Canvas is now GA. dbt Insights, a new AI-powered query interface that helps analysts ask questions and get answers faster, all within dbt. With full awareness of an organization's models, lineage and governance rules, it enables users to query, validate, visualize, and share findings using SQL or natural language in one seamless, governed workspace. This eliminates the need to wait on central data teams to process requests or switch tabs to get answers. dbt Insights is available in preview. An expanded dbt Catalog (formerly dbt Explorer) includes a unified discovery experience that enables global search and exploration for overall Snowflake assets not managed by dbt, offering analysts a comprehensive view of their data landscape. Analysts can easily discover, understand and trust the assets they use, without switching tools. dbt Catalog is now generally available, with the ability to explore Snowflake data assets currently in preview. Integrations for additional data platforms are coming soon. "Lowering the technical barrier to entry for data analysts has been important to Tableau from the beginning of the company," said Dan Jewett, Senior Vice President, Product Management at Tableau. "dbt's expanded offering is a game changer for customers that are looking to reduce the sizable burden on their data engineering teams, while simultaneously enabling analysts across the business in a meaningful way. It's a massive step forward for the future of data teams and one we're thrilled to continue to partner on." dbt Labs customer WHOOP is eager to boost self-service for its analysts, while leaning on easy workflows. "As our data needs evolve, empowering analysts with seamless self-exploration becomes increasingly critical," said William Tsu, Senior Analytics Engineer at WHOOP. "By keeping them within the familiar dbt Catalog they already use daily, dbt's new analyst offerings enhance discoverability and enable faster, more intuitive, and governed self-service." For dbt systems integrator InterWorks, dbt Canvas is poised to remove bottlenecks and power trusted self-service analytics across the organization. "dbt Canvas is unlocking a future where analysts can build confidently alongside engineers within the same trusted and governed workflows," said James Wright, Chief Strategy Officer at InterWorks. "We're excited about how this new development environment will help our customers unlock true self-service while maintaining the standards, security, and collaboration required to scale analytics responsibly." Empowering Organizations to Manage Data Warehouse Spend dbt Labs is also providing new features that allow organizations to optimize data platform costs and ensure the long-term flexibility of their data investments. This includes a cost management dashboard that helps organizations understand data platform costs from their dbt workloads, and also view consumption and realized savings from standardizing on dbt. Powered by the dbt Fusion engine, the cost management dashboard offers visibility into costs at the project, environment, model, and test level, helping users identify and resolve cost inefficiencies. No other vendor owns the transformation workflow from development to production, allowing dbt to embed cost optimization natively rather than as an add-on. The cost management dashboard is in preview for Snowflake customers ahead of the 2025 Snowflake Summit, June 2-5 in San Francisco. A Better-than-ever Developer Experience Announced earlier today, dbt Labs launched the new dbt Fusion engine, incorporating the technology from its acquisition of SDF Labs this year. Fusion delivers massive performance improvements and introduces features that significantly enhance the developer experience. These include next-gen data transformation capabilities that improve code quality by providing real-time feedback, lower costs by avoiding unnecessary warehouse compute, and make dbt 30x faster than dbt Core. For more information on the future of the dbt platform, visit For more information on the new dbt features for analysts, visit *Gartner Insights, Adopt a Data Governance Approach That Enables Business Outcomes, GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. About dbt LabsSince 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Powered by the dbt Fusion engine, it unlocks the performance, context, and trust that organizations need to scale analytics in the era of AI. Globally, more than 60,000 data teams use dbt, including those at Siemens, Roche and Condé Nast. Learn more at and follow dbt Labs on LinkedIn, X, Instagram, and YouTube. View original content to download multimedia: SOURCE dbt Labs Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
6 days ago
- Business
- Yahoo
dbt Labs Redefines dbt with New Fusion Engine, Built to Revolutionize Developer Experience in the Age of AI
New engine enables faster analytics delivery, lower cloud costs, and trusted data pipelines built for AI at scale PHILADELPHIA, May 28, 2025 /PRNewswire/ -- dbt Labs, the leader in standards for AI-ready structured data, today unveiled the new dbt Fusion engine, a monumental evolution of the technology that powers dbt. Fusion, built on Rust and equipped with native SQL comprehension, introduces a lightning-fast developer experience that delivers productivity, data velocity, and platform intelligence to drive substantial cost savings. dbt Labs also launched its VS Code extension, unlocking broad access to the power of Fusion for local developers, and is introducing a free, source-available version of the Fusion engine with a subset of features. These foundational enhancements, along with several others announced today and tailored to bring data analysts into the dbt workflow, will empower organizations to scale analytics in the age of AI. "AI is completely changing the way we interact with data, and dbt is in a prime position to drive the next phase of innovation in the market," said Tristan Handy, founder and CEO, dbt Labs. "Fusion is the most significant evolution of dbt in its history. It gives enterprise data teams the control, speed, and intelligence they need to scale analytics and AI responsibly while keeping costs down." Fusion Engine Brings SQL Comprehension to dbtThe dbt Fusion engine now powers the entire dbt platform, from the CLI that is in use by over 60,000 teams today, to dbt Orchestrator, Catalog, Studio, and the other commercial products powering dbt Labs' rapid growth. Fusion introduces powerful SQL comprehension and a host of other capabilities that collectively deliver a best-in-class developer experience, all while empowering organizations to operate with the highest quality, context-rich data while optimizing costs. New capabilities include lightning-fast parse times, up to 30x faster than dbt Core, allowing large dbt projects to execute in milliseconds instead of minutes. Instant feedback loops and live error detection now uncover and surface parse, compilation and logic errors as code is being written – and before running code against the warehouse – optimizing both developer efficiency and data platform costs. Fusion also brings state-awareness to dbt and with it, a new level of intelligence to how dbt orchestrates pipelines. With state-aware orchestration, available in beta for commercial customers running Fusion, dbt will automatically run jobs as soon as sources are fresh and limit builds to only the models that changed. This helps organizations save on data platform compute and maintain pipeline velocity. Early customer feedback indicates an average 10% cost savings as a result, with additional savings expected as Fusion matures. Organizations can validate these savings in the new cost management dashboard (in preview for Snowflake users), which offers visibility into costs at the warehouse, project, model, and environment level, helping to identify inefficiencies earlier. Other standout functionality includes: Powerful IntelliSense, which autocompletes SQL functions, model names, columns, macros and more; Instant refactoring to rename models or columns and see references update project-wide; Go-to-definition, allowing users to jump to definitions in a single click, a useful feature for large projects with many models and macros; Hover insights, which enable users to see context on tables, columns and functions without leaving code; Live CTE previews directly inside dbt models, for faster validating and debugging; Rich lineage, in context, allowing developers to see lineage at the column or table level as they develop, without breaking flow; and View compiled code, which gives a live view of the SQL code built by models, alongside dbt code. "The data team at Bilt is very excited to roll out the new dbt Fusion engine," said James Dorodo, VP of Data Analytics at Bilt Rewards. "The improvements it brings will address many of the pain points we currently face in our development cycle, and we believe it will provide a step function increase in our velocity." Multiple Paths to Fusion Engine AccessFusion is now available for eligible dbt projects on Snowflake, with support for Databricks, BigQuery, and Redshift coming soon. dbt Labs is also introducing its VS Code extension, the sole way to access the full power of the Fusion engine while developing locally. Now, wherever developers are doing their work, they can do so backed by Fusion. The VS Code extension is downloadable now from the VS Code Marketplace. In addition, dbt Labs is making a subset of Fusion's capabilities broadly available via a new source-available license. This will provide users in the dbt community free access to Fusion's robust developer experience features. AI Adoption Drives The Need For More Quality Data and Unified StandardsA major disruption is underway in analytics, driven by and in service to AI. According to the latest State of Analytics Engineering report, organizations rank AI at the top of their budget priority lists. As these investments grow, the pressure on data teams to deliver trustworthy, contextual data – and a scalable AI strategy – has never been greater. Yet inconsistent standards and fragmented workflows often result in a lack of a single source of truth, making it difficult to scale AI initiatives responsibly. To address this challenge, dbt Labs launched the dbt MCP server, leveraging Model Context Protocol to enable seamless, universal connectivity between AI systems and the governed, structured data in dbt. As the standard for creating governed, trustworthy datasets on top of structured data, dbt unifies models, metrics, documentation, and testing into one collaborative environment. The dbt MCP server gives business users the confidence that all AI endpoints are fueled by context-rich, reliable data, no matter how the AI stack evolves. "dbt Labs' continuous investment in revolutionizing the developer experience aligns well with our commitment to giving customers the very best platform for all of their data engineering needs, with low costs and accelerated performance in the AI Data Cloud," said Chris Child, VP of Product, Data Engineering, Snowflake. "As more of our joint customers adopt AI across their businesses, we know these critical data initiatives require context to be successful. The dbt MCP server complements our investments in AI, and now, with the Fusion engine powering dbt, we're eager to see how much more productive and successful our joint customers will be." dbt Empowers Data Analysts with New FeaturesIn conjunction with the rollout of the Fusion engine, VS Code extension and dbt MCP server, dbt Labs launched a suite of new governed, accessible features designed to bring data analysts into the dbt workflow, including: dbt Canvas, a new AI-powered drag-and-drop visual editing experience that enables analysts less familiar with dbt or SQL to create new and edit existing dbt models within a governed environment. dbt Insights, a new AI-powered query interface that lets analysts perform ad-hoc analysis by asking questions about their data models in SQL or natural language and get answers faster, without waiting on engineering. Extending the functionality of dbt Catalog, formerly known as dbt Explorer, to include search and lineage for overall Snowflake assets alongside your dbt models, making it easier to explore your full data environment. Support for other data platforms is coming soon. These features expand the impact of dbt across the analytics workflow, empowering more collaborators to build, analyze, and explore the data they need, within a governed environment. By supporting governed, scalable self-service, teams reduce engineering bottlenecks, minimize security risks, cut compute costs, and ensure high quality data across their analytics workflow. "Fivetran and dbt have a long history of innovation and delivering on the promise of the modern data stack. With the launch of the dbt Fusion engine and dbt Labs' acquisition of SDF Labs, dbt Labs is accelerating what's possible with data and AI," said Taylor Brown, COO and co-founder, Fivetran. "We're excited to have SDF Labs co-founder Elias DeFaria join us at the Fivetran booth at Snowflake Summit next week to showcase the next wave of tooling." These new features will be the focus of dbt Labs' upcoming presences at Snowflake Summit (June 2-5, booth #1808) and Databricks Summit (June 9-12, booth #326). The dbt Labs team will be onsite at both industry events to discuss and demo the power of Fusion and these new capabilities. To learn more and book a meeting, visit About dbt LabsSince 2016, dbt Labs has been on a mission to help data practitioners create and disseminate organizational knowledge. dbt is the standard for AI-ready structured data. Powered by the dbt Fusion engine, it unlocks the performance, context, and trust that organizations need to scale analytics in the era of AI. Globally, more than 60,000 data teams use dbt, including those at Siemens, Roche and Condé Nast. Learn more at and follow dbt Labs on LinkedIn, X, Instagram, and YouTube. 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